导出用于视频动作识别的模型

使用 export_model 方法导出用于视频操作识别的模型。

深入探索

如需查看包含此代码示例的详细文档，请参阅以下内容：

• 导出 AutoML Edge 模型

代码示例

Java

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.ExportModelOperationMetadata;
import com.google.cloud.aiplatform.v1.ExportModelRequest;
import com.google.cloud.aiplatform.v1.ExportModelResponse;
import com.google.cloud.aiplatform.v1.GcsDestination;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

public class ExportModelVideoActionRecognitionSample {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String modelId = "MODEL_ID";
    String gcsDestinationOutputUriPrefix = "GCS_DESTINATION_OUTPUT_URI_PREFIX";
    String exportFormat = "EXPORT_FORMAT";
    exportModelVideoActionRecognitionSample(
        project, modelId, gcsDestinationOutputUriPrefix, exportFormat);
  }

  static void exportModelVideoActionRecognitionSample(
      String project, String modelId, String gcsDestinationOutputUriPrefix, String exportFormat)
      throws IOException, ExecutionException, InterruptedException {
    ModelServiceSettings settings =
        ModelServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();
    String location = "us-central1";

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ModelServiceClient client = ModelServiceClient.create(settings)) {
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
      ExportModelRequest.OutputConfig outputConfig =
          ExportModelRequest.OutputConfig.newBuilder()
              .setArtifactDestination(gcsDestination)
              .setExportFormatId(exportFormat)
              .build();
      ModelName name = ModelName.of(project, location, modelId);
      OperationFuture<ExportModelResponse, ExportModelOperationMetadata> response =
          client.exportModelAsync(name, outputConfig);

      // You can use OperationFuture.getInitialFuture to get a future representing the initial
      // response to the request, which contains information while the operation is in progress.
      System.out.format("Operation name: %s\n", response.getInitialFuture().get().getName());

      // OperationFuture.get() will block until the operation is finished.
      ExportModelResponse exportModelResponse = response.get();
      System.out.format("exportModelResponse: %s\n", exportModelResponse);
    }
  }
}

Python

在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。

如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。

from google.cloud import aiplatform

def export_model_video_action_recognition_sample(
    project: str,
    model_id: str,
    gcs_destination_output_uri_prefix: str,
    export_format: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.ModelServiceClient(client_options=client_options)
    gcs_destination = {"output_uri_prefix": gcs_destination_output_uri_prefix}
    output_config = {
        "artifact_destination": gcs_destination,
        "export_format_id": export_format,
    }
    name = client.model_path(project=project, location=location, model=model_id)
    response = client.export_model(name=name, output_config=output_config)
    print("Long running operation:", response.operation.name)
    print("output_info:", response.metadata.output_info)
    export_model_response = response.result(timeout=timeout)
    print("export_model_response:", export_model_response)

后续步骤

如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅 Google Cloud 示例浏览器。